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Accurate and fast diagnosis based on histological samples is crucial for prevention, early detection and treatment of cancer. We have developed a digital pathology system that uses machine learning algorithms to analyze images of tissue to assist in cancer diagnosis. This system is used in major diagnostics laboratories in Japan for quality control.
Collaborators: Eric Cosatto & Iain Melvin
Keyword Tag: digital pathology
Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologistreadTIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical
Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction